The present invention generally relates to neuron unit, and more particularly to a neuron unit which resembles neurons and is applicable to neural computers. The present invention also relates to a neuron unit network which includes a plurality of such neuron units which are coupled to form a hierarchical network structure.
Recently, in order to cope with relatively difficult problems encountered in conventional Neumann computers when carrying out a character recognition, an associative storage, a motion control and the like, various models of neural computers have been proposed. The neural computer resembles a nervous system of a living body so that it is possible to realize a parallel processing and a learning function. Various hardware models have also been proposed to realize the neural computer.
FIG. 1 shows an example of a conventional neuron unit proposed in a Japanese Laid-Open Patent Application No. 62-295188. The neuron unit includes a plurality of amplifiers 1 having an S-curve transfer function, and a resistive feedback circuit network 2 which couples outputs of each of the amplifiers 1 to inputs of amplifiers in another layer as indicated by a one-dot chain line. A time constant circuit 3 made up of a grounded capacitor and a grounded resistor is coupled to an input of each of the amplifiers 1. Input currents I.sub.1, I.sub.2, . . . , I.sub.N are respectively applied to the inputs of the amplifiers I, and an output is derived from a collection of output voltages of the amplifiers 1.
An intensity of the coupling (or weighting) between the nervous cells is described by a resistance of a resistor 4 (a lattice point within the resistive feedback circuit network 2) which couples the input and output lines of the nervous cells. A nervous cell response function is described by the transfer function of each amplifier 1. In addition, the coupling between the nervous cells may be categorized into excitation and inhibition couplings, and such couplings are mathematically described by positive and negative signs on weighting coefficients. However, it is difficult to realize the positive and negative values by the circuit constants. Hence, the output of the amplifier 1 is divided into two signals, and one of the two signals is inverted so as to generate a positive signal and a negative signal. One of the positive and negative signals derived from each amplifier 1 is appropriately selected.
FIG. 2 shows a modified version of the neuron unit shown in FIG. 1, and this modified version is proposed in a Japanese Laid-Open Patent Application No. 62-295188. In this case, the neuron unit is simplified based on a mathematical analysis. A negative gain amplifier 5 which produces a single output is used in place of the amplifier 1. In addition, a clipped T matrix circuit 6 is used in place of the resistive feedback circuit network 2.
The conventional neuron units shown in FIGS. 1 and 2 are analog circuits. In other words, the input and output quantities are described in current values or voltage values, and all operations within the circuits are carried out in analog form.
However, it is difficult to ensure accurate and stable operation of the analog circuit because the characteristic of the amplifier changes depending on the temperature, a drift occurs during an initial stage of the circuit operation when the power source is turned ON and the like. Particularly in the case of the neuron unit, at least several hundred amplifiers are required, and the operation stability is critical since a non-linear operation is carried out. In addition, it is difficult to change the circuit constants such as the resistances of the resistors, and the flexibility of the circuit for general applications is poor.
In view of the above, a digital neuron unit is proposed in Hirai et al., "Design of a Completely Digital Neuro-Chip", Technical Report of the Electronic Information and Communication Society, ICD88-130. But this digital neuron unit is simply an emulation of the conventional analog neuron unit, and the circuit construction of the digital neuron unit is quite complex in that up-down counters and the like are required. As a result, it is extremely difficult to provide a learning function in the digital neuron unit.